Download SM-712: Data Mining and Data warehousing Credits: 4 (2-1-2)

SM-712: Data Mining and Data warehousing
Credits: 4 (2-1-2)
Objective:
The main objective of this course is to provide understanding of data warehouse fundamentals and data
mining techniques for business applications.
COURSE DESCRIPTION:
UNIT I:
Data Warehousing: Introduction data warehousing, Data Mart, Data Warehouse Architecture; Star,
Snowflake and Galaxy Schemas for Multidimensional databases, Fact and dimension data, Partitioning
Strategy-Horizontal and Vertical Partitioning. OLAP technology, Multidimensional data models and
different OLAP Operations, OLAP Server: ROLAP, MOLAP, Data Warehouse implementation, Efficient
Computation of Data Cubes, Processing of OLAP queries, Indexing data.
UNIT II:
Data Mining: Basics of data mining, Data mining techniques, KDP (Knowledge Discovery Process),
Application and Challenges of Data Mining; Data Processing: Data Cleaning, Data Integration and
Transformation; Data Reduction: Data Cube Aggregation, Dimensionality reduction, Data Compression,
Numerosity Reduction, Data Discretization and Concept hierarchy generation for numerical and
categorical data. Web Mining: Introduction, Web Content Mining, Web Structure Mining, Web Usage
Mining; Spatial Mining, Text Mining.
UNIT III:
Mining Association Rules in Large Databases: Association Rule Mining, Single-Dimensional Boolean
Association Rules, Multi-Level Association Rule, Apriori Algorithm, FP-Growth Algorithm, Time series
mining association rules, latest trends in association rules mining.
UNIT IV:
Classification methods: Decision tree, Bayesian Classification, Association Rule based; Prediction: Linear
and non-linear regression; Categories of clustering methods, Partitioning methods: K-Means, K-Mediods.
Hierarchical Clustering: Agglomerative and Divisive Clustering, BIRCH and ROCK methods, DBSCAN,
Outlier Analysis. Data Mining for Business Intelligence Applications
Text Books:
1. P.Ponnian, “Data Warehousing Fundamentals”, John Weliey.
2. Han, Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann.
3. P. N. Tan, M. Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education.
4. G. Shmueli, N.R. Patel, P.C. Bruce, “Data Mining for Business Intelligence: Concepts, Techniques,
and Applications in Microsoft Office Excel with XLMiner”, Wiley India.
5. Michael Berry and Gordon Linoff “Data Mining Techniques”,Wiley Publications.
6. M.H.Dunham, “Data Mining Introductory & Advanced Topics”, Pearson Education.
Course Plan:
Week Unit
1
I
2
I
3
4
I
II
5
II
6
II
7
II
8
III
9
III
10
III
11
IV
12
IV
13
IV
14
IV
15
IV
16
TOTAL
Topics
Lecture
Data Warehousing: Introduction data warehousing, 2
Data Mart, Data Warehouse Architecture; Star,
Snowflake and Galaxy Schemas for Multidimensional
databases.
OLAP technology, Multidimensional data models and 2
different OLAP Operations.
Data Warehousing, Data Mining, OLTP, OLAP.
2
Data Mining: Basics of data mining, Data mining 2
techniques, KDP (Knowledge Discovery Process),
Application and Challenges of Data Mining.
Data Processing: Data Cleaning, Data Integration 2
and Transformation; Data Reduction: Data Cube
Aggregation, Dimensionality reduction.
Data Compression, Numerosity Reduction, Data 2
Discretization and Concept hierarchy generation for
numerical and categorical data. Introduction to Web
Mining .
Web Content Mining, Web Structure Mining, Web 2
Usage Mining; Spatial Mining, Text Mining
Mining Association Rules in Large Databases: 2
Association Rule Mining, Single-Dimensional Boolean
Association Rules, Multi-Level Association Rule.
Apriori Algorithm, FP-Growth Algorithm, Time series 2
mining association rules.
latest trends in association rules mining,
2
Practical case studies based on association rules.
Classification methods: Decision tree, Bayesian 2
Classification, Association Rule based; Prediction:
Linear and non-linear regression;
JD Edwards, QAD Inc, SSA Global, Lawson Software, 2
Baan,Enterprise, Epicor, Intutive.
Partitioning methods: K-Means, K-Mediods. 2
Hierarchical Clustering: Agglomerative and Divisive
Clustering.
BIRCH and ROCK methods, DBSCAN, Outlier Analysis. 2
Data Mining for Business Intelligence Applications
Case studies of business analysis using data mining 2
techniques.
Revision
2
32
Hours
Tutorial
1
Practical
2
1
2
1
1
2
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
2
1
16
2
32